In this post, a validation of the land-cover map of France produced by CESBIO for the 2016 period was presented. This validation used independent data (that is data collected by different teams and using different procedures than the data used for the classifier training), but the validation procedure consisted in applying classical machine learning metrics which, as described in this other post, have some limitations.

A fully independent validation following a sound protocol is costly and needs skills and expertise that are very specific. SIRS is a company which is specialised in the production of geographic data from satellite or aerial images. Among other things, they are the producers of Corine Land Cover for France and they are also responsible for quality control and validation of other Copernicus Land products.

SIRS has recently performed a validation of the 2016 France land-cover map. The executive summary of the report reads as follows:

This report provides the evaluation results of the CESBIO OSO 2016 10m layer and the CESBIO OSO 2016 20m layer.

The thematic accuracy assessment was conducted in a two-stage process:

An initial blind interpretation in which the validation team did not have knowledge of the product’s thematic classes.

A plausibility analysis was performed on all sample units in disagreement with the production data to consider the following cases:

Uncertain code, both producer and operator codes are plausible. Final validation code used is producer code.

Error from first validation interpretation. Final validation used is producer code

Error from producer. Final validation code used is from first validation interpretation

Producer and operator are both wrong. Final Validation code used is a new code from this second interpretation.

Resulting to this two-stage approach, it should be noticed that the plausibility analysis exhibit better results than the blind analysis.

The thematic accuracy assessment was carried out over 1,428 sample units covering France and Corsica.
The final results show that the CESBIO OSO product meet the usually accepted thematic validation requirement, i.e. 85 % in both blind interpretation and plausibility analysis. Indeed, the overall accuracies obtained are 81.4 +/- 3.68% for the blind analysis and 91.7 +/- 1.25% for the plausibility analysis on the CESBIO OSO 10m layer. The analysis on the 20m layer shows us that the overall accuracy for the blind approach is 81.1 +/-3.65% and 88.2 +/-3.15% for the plausibility approach.
Quality checks of the validation points have been made by French experts. It should be noticed that for the blind analysis, the methodology of control was based mostly on Google Earth imagery, no additional thematic source of information that could provide further context was used such as forest stand maps, peatland maps, etc.

These results are very good news for us and for our users. The report also contains interesting recommendations that will help us to improve our algorithms. The full report is available for download.

Great news, we can announce that the operational production of the Theia snow collection has started well. It means that maps of the snow cover area are now constantly added to the Theia portal. These maps are automatically generated from Sentinel-2 observations and have a spatial resolution of 20 m. The Snow collection will progressively cover most mountain regions in west Europe, but also the Atlas in Morocco, eastern Canada... The Snow collection can be freely downloaded from http://theia.cnes.fr by any registered user.

Today's front page of the Theia website featured this nice example in Sierra de Ancares (western end of the Cantabrian Mountains, Spain). In the southeast, snow was also detected on the Montes Aquilanos, including the small ski resort El Morredero. The image was captured yesterday! It illustrates well the value of multispectral imagery to discriminate the snow cover from the clouds. There is a cloud which looks alike snow but it is actually a valley fog confined by local topography.

You probably remember Simon Gascoin's story about the Aru glacier avalanches, which started from Simon's observations of the twin avalanches using the Sentinels. It was one of the big buzz pages of the blog in 2016. The first images were published here, then spread out in many scientific websites and the social networks.

The same mountain valley in Tibet is shown before and after part of a glacier sheared off on 17 July 2016. Credit: NASA/Joshua Stevens/USGS/ESA

It seems that the story finally made its way to Nature Geoscience, after a large work from many scientists lead by Andreas Kaab. Congratulations to all the team !

So, dear CESBIO colleagues, or remote sensing time series users, it is time to submit your work to this blog as a first step to future publications in Nature !

Land cover map validation is a complex task. If you read French, you can check this post by Vincent Thierion which shows how the 2016 LC map of France produced by CESBIO stands with respect to data sources independent from those used for its production. But this is only one aspect of the validation. A land cover map is a map, and therefore, there are other issues than checking if individual points belong to the correct class. By the way, being sure that the correct class is known, is not so easy neither.

In this epoch of machine learning hype 1, it is easy to fall in the trap of thinking that optimising a single metric accounts for all issues in map validation. Typical approaches used in machine learning contests are far from enough for this complex task. Let's have a look at how we proceed at CESBIO when we assess the quality of a LC map produced by classification.Continue reading

For Christmas Sentinel-2 offered us this nice picture of the Bezymianny volcano ash trail. Bezymianny is located in the central part of the Klyuchevskaya cluster of volcanoes in the Kamachatka peninsula. The image was acquired on Christmas eve but the ash plume was observed on 20 December.

The land surface models simulate the water and energy fluxes between soil, land cover and atmosphere. Their scopes of application spread from numerical weather prevision to soil water modeling.

Energy and water budgets of the soil-plant continuum

However, these models are initially conceived to be applied on wide areas. Thus, they use low resolution cover parameters (>1km) derived from mid-resolution satellite observations (MODIS, VEGETATION). These parameters are mainly the Leaf Area Index (LAI), the vegetation type or the surface albedo. Yet the agricultural landscapes of Western Europe are characterized by a patchwork of plots smaller than one square kilometer. These plots have very different vegetation cycles, i.e. winter and summer crops, which could only be described at high resolution. The crop management practices like crop rotation or irrigation are also generally not taken into account.

The products of the Sentinel-2 space mission, with their high spatial and temporal resolution, could bring elements to fill in this missing information.